• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.875-878
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• 1998

In this paper, an adaptive event detection algorithm is proposed, for which we use the statistics of subblock image and adaptive threshold levels. The adaptive threshold level for a parameter binarization is taken by averaging the corresponding paramerter obtained from several input images. As simulation results, it is shown that the proposed algorithm is much more adaptive to the input images and effective in event detection rate than the conventional difference based algorithms.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.6
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• pp.213-224
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• 2003

The proposed algorithm can always set up the optimal threshold value regardless of channel characteristics using an adaptive threshold establishment method that determines the threshold level according to channel noise power, and then it uses the specially designed training symbols that can make the algorithm's estimation performance be less sensitive to power delay profile variation in a multipath channel. In result, the estimation performance of the proposed technique is less affected by channel characteristic variation.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.891-894
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• 2004

This paper proposes an improved image segmentation algorithm by the watershed algorithm based on the local adaptive threshold on local minima search and the fixing threshold on label allocation. The previous watershed algorithm generates the problem of over-segmentation. The over-segmentation makes the boundary in the inaccuracy region by occurring around the object. In order to solve those problems we quantize the input color image by the vector quantization, remove noise and find the gradient image. We sorted local minima applying the local adaptive threshold on local minima search of the input color image. The simulation results show that the proposed algorithm controls over-segmentation and makes the fine boundary around segmented region applying the fixing threshold based on sorted local minima on label allocation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.229-232
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• 2009

Shot change detection is an important technique for effective management of video data, so detection scheme requires adaptive detection techniques to be used actually in various video. In this paper, we propose an adaptive shot change detection algorithm using the mean of feature value on variable reference blocks. Our algorithm determines shot change detection by defining adaptive threshold values with the feature value extracted from video frames and comparing the feature value and the threshold value. We obtained better detection ratio than the conventional methods maximally by 15% in the experiment with the same test sequence. We also had good detection ratio for other several methods of feature extraction and could see real-time operation of shot change detection in the hardware platform with low performance was possible by implementing it in TVUS model of HOMECAST Company. Thus, our algorithm in the paper can be useful in PMP or other portable players.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.179-185
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• 2019

This paper proposes a block-type classification based image binarization for the implementation of the low-power feature extraction algorithm. The proposed method can be implemented with threshold value re-use technique approach when the image divided into $64{\times}64$ macro blocks size and calculating the threshold value for each block type only once. The algorithm is validated based on quantitative results that only a threshold value change rate of up to 9% occurs within the same image/block type. Existing algorithms should compute the threshold value for 64 blocks when the macro block is divided by $64{\times}64$ on the basis of $512{\times}512$ images, but all suggestions can be made only once for best cases where the same block type is printed, and for the remaining 63 blocks, the adaptive threshold calculation can be reduced by only performing a block type classification process. The threshold calculation operation is performed five times when all block types occur, and only the block type separation process can be performed for the remaining 59 blocks, so 93% adaptive threshold calculation operation can be reduced.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.10
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• pp.809-814
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• 2012

We propose an adaptive threshold detector algorithm for multi-level holographic data storage based on the expectation-maximization (EM) method. In this paper, the signal intensities that are passed through the four-level holographic channel are modeled as a four Gaussian mixture with unknown DC offsets and the threshold levels are estimated based on the maximum likelihood criterion. We compare the bit error rate (BER) performance of the proposed algorithm with the non-adaptive threshold detection algorithm for various levels of DC offset and misalignments. Our proposed algorithm shows consistently acceptable performance when the DC offset variance is fixed or the misalignments are lower than 20%. When the DC offset varies with each page, the BER of the proposed method is acceptable when the misalignments are lower than 10% and DC offset variance is 0.001.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.17-26
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• 2006

The error diffusion method is one of the digital halftoning processes that transforms the continuous-tone image to the binary image and the method has the excellent reproduction ability. However the error diffusion method using the permanent threshold has difficulty in proper binarization, so the method has the periodic pattern and is unpleasant to the eye. In this paper, to reduce defects and to binarize properly, we propose the error diffusion method using the adaptive threshold. Depending on the intensity distribution of the input gray scale image, we decided on the adaptive threshold with the average of the intensities. The error diffusion method with the adaptive threshold has the better performance than the existing method and is evaluated with experiments and comparisons.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.5
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• pp.632-639
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• 2012

Ad-hoc networks can be used various environment, which it is difficult to construct infrastructures, such as shadowing areas, disaster areas, war area, and so on. In order to support to considerable and various wireless services, more spectrum resources are needed. However, efficient utilization of the frequency resource is difficult because of spectrum scarcity and the conventional frequency regulation. Ad-hoc networks employing cognitive radio(CR) system that guarantee high spectrum utilization provide effective way to increase the network capacity. In conventional CR based ad-hoc network, it uses constant threshold value to detect primary user signal, so the results become not reliable. In this paper, to solve this problem, we apply adaptive threshold value to the CR based ad-hoc network, and adaptive threshold is immediately changed by SNR(Signal to Noise Ratio). From the simulation results, we confirmed that proposed algorithm has the greatly better detection probabilities than conventional CR based ad-hoc network.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.756-763
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• 2020

Objective: To evaluate the effects of attenuation threshold on CT pulmonary vascular volume ratios in children and young adults with congenital heart disease, and to suggest an optimal attenuation threshold. Materials and Methods: CT percentages of right pulmonary vascular volume were compared and correlated with percentages calculated from nuclear medicine right lung perfusion in 52 patients with congenital heart disease. The selected patients had undergone electrocardiography-synchronized cardiothoracic CT and lung perfusion scintigraphy within a 1-year interval, but not interim surgical or transcatheter intervention. The percentages of CT right pulmonary vascular volumes were calculated with fixed (80-600 Hounsfield units [HU]) and adaptive thresholds (average pulmonary artery enhancement [PA_avg] divided by 2.50, 2.00, 1.75, 1.63, 1.50, and 1.25). The optimal threshold exhibited the smallest mean difference, the lowest p-value in statistically significant paired comparisons, and the highest Pearson correlation coefficient. Results: The PA_avg value was 529.5 ± 164.8 HU (range, 250.1-956.6 HU). Results showed that fixed thresholds in the range of 320-400 HU, and adaptive thresholds of PA_avg/1.75-1.50 were optimal for quantifying CT pulmonary vascular volume ratios. The optimal thresholds demonstrated a small mean difference of ≤ 5%, no significant difference (> 0.2 for fixed thresholds, and > 0.5 for adaptive thresholds), and a high correlation coefficient (0.93 for fixed thresholds, and 0.91 for adaptive thresholds). Conclusion: The optimal fixed and adaptive thresholds for quantifying CT pulmonary vascular volume ratios appeared equally useful. However, when considering a wide range of PAavg, application of optimal adaptive thresholds may be more suitable than fixed thresholds in actual clinical practice.

• Proceedings of the IEEK Conference
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• summer
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• pp.438-441
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• 2004

Image binarization using a global threshold value [3] performs at high speed, but usually results in undesired binary images when the source images are of poor quality. In such cases, adaptive local thresholding algorithms [1][2][3] are used to obtain better results, and the algorithm proposed by A.E.Savekis which chooses local threshold using fore­ground and background clustering [1] is one of the best thresholding algorithms. However, this algorithm runs slowly due to its re-computing threshold value of each central pixel in a local window MxM. In this paper, we present a dynamic programming approach for the step of calculating local threshold value that reduces many redundant computations and improves the execution speed significantly. Experiments show that our proposal improvement runs more ten times faster than the original algorithm.